{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ReservoirHeterogeneity.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPEsbC/k9ZnvCo+XueXFEtH",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/ReservoirHeterogeneity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Ef0OrUnvKCl",
        "colab_type": "text"
      },
      "source": [
        "#There are essentially two types of heterogeneity:\n",
        "• Vertical heterogeneity\n",
        "• Areal heterogeneity\n",
        "\n",
        "#Vertical Heterogeneity\n",
        "One of the first problems encountered by the reservoir engineer in predicting or interpreting fluid displacement behavior during secondary\n",
        "recovery and enhanced oil recovery processes is that of organizing and\n",
        "using the large amount of data available from core analysis. Permeabilities pose particular problems in organization because they usually vary\n",
        "by more than an order of magnitude between different strata. The engineer must be able then to:\n",
        "• Describe the degree of the vertical heterogeneity in mathematical\n",
        "terms, and\n",
        "• Describe and define the proper permeability stratification of the pay\n",
        "zone. This task is commonly called the zoning or layering problem.\n",
        "\n",
        "The following are the two most widely used descriptors of\n",
        "the vertical heterogeneity of the formation:\n",
        "#1. Dykstra-Parsons permeability variation V\n",
        "#2. Lorenz coefficient L"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EYGa6j5OxYwR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#STEP 1 : The Data"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkrqGaCouxGB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "%matplotlib inline"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYfl0KqI0_Xn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('Perm.csv.txt',sep=' ',index_col=None)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7WcPcYKc1MoL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "kh_data = df[['a','b']]"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQklJXln1NxQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "kh_data.columns = ['k','h']"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpSg2ayk4YAh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "012ce53b-37c7-4601-f9cd-e17d34b9d99a"
      },
      "source": [
        "total_thickness = kh_data['h'].sum()\n",
        "print(f'The total reservoir thickness is= {total_thickness} ft.')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The total reservoir thickness is= 56.0 ft.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7dZPKKt4ZUv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "h = list(kh_data['h'])\n",
        "\n",
        "sum = 0\n",
        "cum_h = [0]\n",
        "\n",
        "for i in range(len(h)-1):\n",
        "  sum = sum + h[i]\n",
        "  cum_h.append(sum)\n",
        "\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Op03C_J5ZD5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "4fba0d32-dbc0-4fd0-ba4d-0b679e025216"
      },
      "source": [
        "kh_data['h with greater k'] = cum_h"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zg75FYxA6cW5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "kh_data['%h with greater k'] = kh_data['h with greater k'].apply(lambda x : (x/56)*100)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNyNqYBp7xx8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "outputId": "44b01737-5130-49b2-ea86-902379aaf204"
      },
      "source": [
        "kh_data"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>k</th>\n",
              "      <th>h</th>\n",
              "      <th>h with greater k</th>\n",
              "      <th>%h with greater k</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>435</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>254</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>196</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.142857</td>\n",
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              "      <th>3</th>\n",
              "      <td>172</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>166</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>16.071429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>158</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>19.642857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>153</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>23.214286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>147</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>26.785714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>128</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>30.357143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>105</td>\n",
              "      <td>2.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>32.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>100</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>35.714286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>91</td>\n",
              "      <td>2.5</td>\n",
              "      <td>21.0</td>\n",
              "      <td>37.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>90</td>\n",
              "      <td>2.5</td>\n",
              "      <td>23.5</td>\n",
              "      <td>41.964286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>83</td>\n",
              "      <td>2.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>46.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>72</td>\n",
              "      <td>1.5</td>\n",
              "      <td>28.0</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>62</td>\n",
              "      <td>2.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>52.678571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>49</td>\n",
              "      <td>6.5</td>\n",
              "      <td>31.5</td>\n",
              "      <td>56.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>44</td>\n",
              "      <td>2.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>67.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>40</td>\n",
              "      <td>2.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>71.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>35</td>\n",
              "      <td>2.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>75.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>32</td>\n",
              "      <td>2.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>78.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>30</td>\n",
              "      <td>2.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>82.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>28</td>\n",
              "      <td>2.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>85.714286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>27</td>\n",
              "      <td>2.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>89.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>20</td>\n",
              "      <td>2.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>92.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>96.428571</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      k    h  h with greater k  %h with greater k\n",
              "0   435  2.0               0.0           0.000000\n",
              "1   254  2.0               2.0           3.571429\n",
              "2   196  2.0               4.0           7.142857\n",
              "3   172  3.0               6.0          10.714286\n",
              "4   166  2.0               9.0          16.071429\n",
              "5   158  2.0              11.0          19.642857\n",
              "6   153  2.0              13.0          23.214286\n",
              "7   147  2.0              15.0          26.785714\n",
              "8   128  1.0              17.0          30.357143\n",
              "9   105  2.0              18.0          32.142857\n",
              "10  100  1.0              20.0          35.714286\n",
              "11   91  2.5              21.0          37.500000\n",
              "12   90  2.5              23.5          41.964286\n",
              "13   83  2.0              26.0          46.428571\n",
              "14   72  1.5              28.0          50.000000\n",
              "15   62  2.0              29.5          52.678571\n",
              "16   49  6.5              31.5          56.250000\n",
              "17   44  2.0              38.0          67.857143\n",
              "18   40  2.0              40.0          71.428571\n",
              "19   35  2.0              42.0          75.000000\n",
              "20   32  2.0              44.0          78.571429\n",
              "21   30  2.0              46.0          82.142857\n",
              "22   28  2.0              48.0          85.714286\n",
              "23   27  2.0              50.0          89.285714\n",
              "24   20  2.0              52.0          92.857143\n",
              "25    9  2.0              54.0          96.428571"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cdxy9ObR8qjQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Hence our required dataset is prepared"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kia5scD80RB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Step 2: Plotting"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9z_OE5ci9Mtd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "e84cdddb-cb51-41d2-b050-0cc6f837aa33"
      },
      "source": [
        "kh_data['%h with greater k'][0] = 1\n"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DyTuS0bF9pTq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = kh_data[['%h with greater k']].values\n",
        "y = kh_data[['k']].values\n",
        "y = np.array(y).reshape(26,)"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xaAWV3EeftjT",
        "colab_type": "text"
      },
      "source": [
        "# Evaluating k50 and k84.1 since - \n",
        "The Dykstra-Parsons permeability variation is defined by the following expression:\n",
        "# V = [k(50) - k(84.1)]/k(50)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgw1b-qgf4dB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = np.log(X).reshape(26,)\n",
        "slope, intercept, r_value, p_value, std_err = stats.linregress(X,y)"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Usuu9qBghqy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "84a1612c-978f-4c92-9fe6-8f2a80dbebb5"
      },
      "source": [
        "k50 = slope*np.log(50) + intercept\n",
        "print(f\"k50 = {k50} md\")\n",
        "k84_1 = slope*np.log(84.1) + intercept\n",
        "print(f\"k84.1 = {k84_1} md\")"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "k50 = 70.55707045142475 md\n",
            "k84.1 = 27.361113180695554 md\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wh3-CfhK3NWl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ca62dc7a-ace3-497b-95a4-61c0b2c1a411"
      },
      "source": [
        "#Hence,\n",
        "V = (k50 - k84_1)/k50\n",
        "print(f\"The Dykstra-Parsons permeability variation is = {V}\")"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Dykstra-Parsons permeability variation is = 0.6122130212374336\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LaWD5Ohp3j0W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "yp = slope*X + intercept"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3i4svIW4XUUR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "a038be26-be93-419d-c903-97d772f343c5"
      },
      "source": [
        "sns.regplot(X,y,)\n",
        "plt.xlabel('Log of Probablity (of % h)')\n",
        "plt.ylabel('K, milidarcy')\n",
        "plt.title(\"Dykstra-Psrson's plot\" )"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, \"Dykstra-Psrson's plot\")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1FLr-IC3mPq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Note that you can either take the log of x-values and plot on cartesian or \n",
        "# take the original values with x-axis being logarithmic. Same thing!\n"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bMnZRL4YkNex",
        "colab_type": "text"
      },
      "source": [
        "#2. Lorrenz Plot\n",
        "\n",
        "1. Lorenz coefficient in terms of permeability variation:\n",
        "L = 0.0116356 + 0.339794*V + 1.066405*V^2 - 0.3852407*V^3 \n",
        "\n",
        "2. Permeability variation in terms of Lorenz coefficient:\n",
        "V = -5.05971*(10-4) + 1.747525L - 1.468855*L^2 + 0.701023*L^3 \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfV4XGik3tP_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "L = 0.0116356 + 0.339794*V + 1.066405*(V**2) - 0.3852407*(V**3)"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zY8odwla4DsX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bfa1eee4-f7fc-45a8-82c6-612461d7184a"
      },
      "source": [
        "print(L)"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5309581332510235\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1xKxUud4FHZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Let's do this in a more explainable way!"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8unLjcd7aN3t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lp_data = kh_data[['k','h']]"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZZSTbx8b-oL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "h = list(lp_data['h'])\n",
        "\n",
        "sum = 2\n",
        "cum_h = [2]\n",
        "\n",
        "for i in range(1,len(h)):\n",
        "  sum = sum + h[i]\n",
        "  cum_h.append(sum)"
      ],
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9CwYQpTTm0NS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "c0975076-f776-4b14-c7b5-c235efa895b5"
      },
      "source": [
        "lp_data['kh'] = lp_data['k']*lp_data['h']\n",
        "lp_data.head(2)"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>k</th>\n",
              "      <th>h</th>\n",
              "      <th>kh</th>\n",
              "      <th>cum_h</th>\n",
              "      <th>cum_kh</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>435</td>\n",
              "      <td>2.0</td>\n",
              "      <td>870.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>870.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>254</td>\n",
              "      <td>2.0</td>\n",
              "      <td>508.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1378.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     k    h     kh  cum_h  cum_kh\n",
              "0  435  2.0  870.0    0.0   870.0\n",
              "1  254  2.0  508.0    2.0  1378.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcBYfeyRdLGL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "kh = list(lp_data['kh'])\n",
        "sum1 = 870\n",
        "cum_kh = [870]\n",
        "\n",
        "for j in range(1,len(kh)):\n",
        "  sum1 = sum1 + kh[j]\n",
        "  cum_kh.append(sum1)"
      ],
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9raQ_EonNDx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lp_data['cum_h'] = cum_h"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2H-bdVOAnWuA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lp_data['cum_kh'] = cum_kh"
      ],
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQpY-00Ona_K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "outputId": "0bbe31e2-5e1d-46bd-9877-570fcb0901b5"
      },
      "source": [
        "lp_data"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>k</th>\n",
              "      <th>h</th>\n",
              "      <th>kh</th>\n",
              "      <th>cum_h</th>\n",
              "      <th>cum_kh</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>435</td>\n",
              "      <td>2.0</td>\n",
              "      <td>870.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>870.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>254</td>\n",
              "      <td>2.0</td>\n",
              "      <td>508.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1378.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>196</td>\n",
              "      <td>2.0</td>\n",
              "      <td>392.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1770.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>172</td>\n",
              "      <td>3.0</td>\n",
              "      <td>516.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2286.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>166</td>\n",
              "      <td>2.0</td>\n",
              "      <td>332.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2618.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>158</td>\n",
              "      <td>2.0</td>\n",
              "      <td>316.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2934.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>153</td>\n",
              "      <td>2.0</td>\n",
              "      <td>306.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>147</td>\n",
              "      <td>2.0</td>\n",
              "      <td>294.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3534.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>128</td>\n",
              "      <td>1.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>3662.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>105</td>\n",
              "      <td>2.0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3872.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>100</td>\n",
              "      <td>1.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>3972.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>91</td>\n",
              "      <td>2.5</td>\n",
              "      <td>227.5</td>\n",
              "      <td>23.5</td>\n",
              "      <td>4199.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>90</td>\n",
              "      <td>2.5</td>\n",
              "      <td>225.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>4424.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>83</td>\n",
              "      <td>2.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>4590.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>72</td>\n",
              "      <td>1.5</td>\n",
              "      <td>108.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>4698.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>62</td>\n",
              "      <td>2.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>31.5</td>\n",
              "      <td>4822.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>49</td>\n",
              "      <td>6.5</td>\n",
              "      <td>318.5</td>\n",
              "      <td>38.0</td>\n",
              "      <td>5141.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>44</td>\n",
              "      <td>2.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>5229.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>40</td>\n",
              "      <td>2.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>5309.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>35</td>\n",
              "      <td>2.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>5379.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>32</td>\n",
              "      <td>2.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>5443.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>30</td>\n",
              "      <td>2.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>5503.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>28</td>\n",
              "      <td>2.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>5559.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>27</td>\n",
              "      <td>2.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5613.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>20</td>\n",
              "      <td>2.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>5653.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>5671.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      k    h     kh  cum_h  cum_kh\n",
              "0   435  2.0  870.0    2.0   870.0\n",
              "1   254  2.0  508.0    4.0  1378.0\n",
              "2   196  2.0  392.0    6.0  1770.0\n",
              "3   172  3.0  516.0    9.0  2286.0\n",
              "4   166  2.0  332.0   11.0  2618.0\n",
              "5   158  2.0  316.0   13.0  2934.0\n",
              "6   153  2.0  306.0   15.0  3240.0\n",
              "7   147  2.0  294.0   17.0  3534.0\n",
              "8   128  1.0  128.0   18.0  3662.0\n",
              "9   105  2.0  210.0   20.0  3872.0\n",
              "10  100  1.0  100.0   21.0  3972.0\n",
              "11   91  2.5  227.5   23.5  4199.5\n",
              "12   90  2.5  225.0   26.0  4424.5\n",
              "13   83  2.0  166.0   28.0  4590.5\n",
              "14   72  1.5  108.0   29.5  4698.5\n",
              "15   62  2.0  124.0   31.5  4822.5\n",
              "16   49  6.5  318.5   38.0  5141.0\n",
              "17   44  2.0   88.0   40.0  5229.0\n",
              "18   40  2.0   80.0   42.0  5309.0\n",
              "19   35  2.0   70.0   44.0  5379.0\n",
              "20   32  2.0   64.0   46.0  5443.0\n",
              "21   30  2.0   60.0   48.0  5503.0\n",
              "22   28  2.0   56.0   50.0  5559.0\n",
              "23   27  2.0   54.0   52.0  5613.0\n",
              "24   20  2.0   40.0   54.0  5653.0\n",
              "25    9  2.0   18.0   56.0  5671.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NMv3OrLondT8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lp_data['Norm_kh'] = lp_data['cum_kh']/5671.0"
      ],
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVGX4kDlpcCZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lp_data['Norm_h'] = lp_data['cum_h']/56.0"
      ],
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TU4fip1ArCUu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "outputId": "4702d21b-3f91-4810-d980-2644fc046362"
      },
      "source": [
        "lp_data"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>k</th>\n",
              "      <th>h</th>\n",
              "      <th>kh</th>\n",
              "      <th>cum_h</th>\n",
              "      <th>cum_kh</th>\n",
              "      <th>Norm_kh</th>\n",
              "      <th>Norm_h</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>435</td>\n",
              "      <td>2.0</td>\n",
              "      <td>870.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>870.0</td>\n",
              "      <td>0.153412</td>\n",
              "      <td>0.035714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>254</td>\n",
              "      <td>2.0</td>\n",
              "      <td>508.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1378.0</td>\n",
              "      <td>0.242991</td>\n",
              "      <td>0.071429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>196</td>\n",
              "      <td>2.0</td>\n",
              "      <td>392.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1770.0</td>\n",
              "      <td>0.312114</td>\n",
              "      <td>0.107143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>172</td>\n",
              "      <td>3.0</td>\n",
              "      <td>516.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2286.0</td>\n",
              "      <td>0.403104</td>\n",
              "      <td>0.160714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>166</td>\n",
              "      <td>2.0</td>\n",
              "      <td>332.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2618.0</td>\n",
              "      <td>0.461647</td>\n",
              "      <td>0.196429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>158</td>\n",
              "      <td>2.0</td>\n",
              "      <td>316.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2934.0</td>\n",
              "      <td>0.517369</td>\n",
              "      <td>0.232143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>153</td>\n",
              "      <td>2.0</td>\n",
              "      <td>306.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3240.0</td>\n",
              "      <td>0.571328</td>\n",
              "      <td>0.267857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>147</td>\n",
              "      <td>2.0</td>\n",
              "      <td>294.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3534.0</td>\n",
              "      <td>0.623171</td>\n",
              "      <td>0.303571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>128</td>\n",
              "      <td>1.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>3662.0</td>\n",
              "      <td>0.645741</td>\n",
              "      <td>0.321429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>105</td>\n",
              "      <td>2.0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3872.0</td>\n",
              "      <td>0.682772</td>\n",
              "      <td>0.357143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>100</td>\n",
              "      <td>1.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>3972.0</td>\n",
              "      <td>0.700406</td>\n",
              "      <td>0.375000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>91</td>\n",
              "      <td>2.5</td>\n",
              "      <td>227.5</td>\n",
              "      <td>23.5</td>\n",
              "      <td>4199.5</td>\n",
              "      <td>0.740522</td>\n",
              "      <td>0.419643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>90</td>\n",
              "      <td>2.5</td>\n",
              "      <td>225.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>4424.5</td>\n",
              "      <td>0.780197</td>\n",
              "      <td>0.464286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>83</td>\n",
              "      <td>2.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>4590.5</td>\n",
              "      <td>0.809469</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>72</td>\n",
              "      <td>1.5</td>\n",
              "      <td>108.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>4698.5</td>\n",
              "      <td>0.828513</td>\n",
              "      <td>0.526786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>62</td>\n",
              "      <td>2.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>31.5</td>\n",
              "      <td>4822.5</td>\n",
              "      <td>0.850379</td>\n",
              "      <td>0.562500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>49</td>\n",
              "      <td>6.5</td>\n",
              "      <td>318.5</td>\n",
              "      <td>38.0</td>\n",
              "      <td>5141.0</td>\n",
              "      <td>0.906542</td>\n",
              "      <td>0.678571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>44</td>\n",
              "      <td>2.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>5229.0</td>\n",
              "      <td>0.922060</td>\n",
              "      <td>0.714286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>40</td>\n",
              "      <td>2.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>5309.0</td>\n",
              "      <td>0.936166</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>35</td>\n",
              "      <td>2.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>5379.0</td>\n",
              "      <td>0.948510</td>\n",
              "      <td>0.785714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>32</td>\n",
              "      <td>2.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>5443.0</td>\n",
              "      <td>0.959795</td>\n",
              "      <td>0.821429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>30</td>\n",
              "      <td>2.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>5503.0</td>\n",
              "      <td>0.970376</td>\n",
              "      <td>0.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>28</td>\n",
              "      <td>2.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>5559.0</td>\n",
              "      <td>0.980250</td>\n",
              "      <td>0.892857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>27</td>\n",
              "      <td>2.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5613.0</td>\n",
              "      <td>0.989773</td>\n",
              "      <td>0.928571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>20</td>\n",
              "      <td>2.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>5653.0</td>\n",
              "      <td>0.996826</td>\n",
              "      <td>0.964286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>5671.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      k    h     kh  cum_h  cum_kh   Norm_kh    Norm_h\n",
              "0   435  2.0  870.0    2.0   870.0  0.153412  0.035714\n",
              "1   254  2.0  508.0    4.0  1378.0  0.242991  0.071429\n",
              "2   196  2.0  392.0    6.0  1770.0  0.312114  0.107143\n",
              "3   172  3.0  516.0    9.0  2286.0  0.403104  0.160714\n",
              "4   166  2.0  332.0   11.0  2618.0  0.461647  0.196429\n",
              "5   158  2.0  316.0   13.0  2934.0  0.517369  0.232143\n",
              "6   153  2.0  306.0   15.0  3240.0  0.571328  0.267857\n",
              "7   147  2.0  294.0   17.0  3534.0  0.623171  0.303571\n",
              "8   128  1.0  128.0   18.0  3662.0  0.645741  0.321429\n",
              "9   105  2.0  210.0   20.0  3872.0  0.682772  0.357143\n",
              "10  100  1.0  100.0   21.0  3972.0  0.700406  0.375000\n",
              "11   91  2.5  227.5   23.5  4199.5  0.740522  0.419643\n",
              "12   90  2.5  225.0   26.0  4424.5  0.780197  0.464286\n",
              "13   83  2.0  166.0   28.0  4590.5  0.809469  0.500000\n",
              "14   72  1.5  108.0   29.5  4698.5  0.828513  0.526786\n",
              "15   62  2.0  124.0   31.5  4822.5  0.850379  0.562500\n",
              "16   49  6.5  318.5   38.0  5141.0  0.906542  0.678571\n",
              "17   44  2.0   88.0   40.0  5229.0  0.922060  0.714286\n",
              "18   40  2.0   80.0   42.0  5309.0  0.936166  0.750000\n",
              "19   35  2.0   70.0   44.0  5379.0  0.948510  0.785714\n",
              "20   32  2.0   64.0   46.0  5443.0  0.959795  0.821429\n",
              "21   30  2.0   60.0   48.0  5503.0  0.970376  0.857143\n",
              "22   28  2.0   56.0   50.0  5559.0  0.980250  0.892857\n",
              "23   27  2.0   54.0   52.0  5613.0  0.989773  0.928571\n",
              "24   20  2.0   40.0   54.0  5653.0  0.996826  0.964286\n",
              "25    9  2.0   18.0   56.0  5671.0  1.000000  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1zKi7eBrE8_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "outputId": "a6b35fa0-cf8d-4949-d99e-87a24e994d2a"
      },
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "plt.grid()\n",
        "plt.plot(lp_data['Norm_h'],lp_data['Norm_kh'],label='Lorenz Plot for a hetero. reservoir')\n",
        "plt.plot(lp_data['Norm_h'],lp_data['Norm_h'],label='Lorenz plot for homog. res.')\n",
        "plt.legend()\n",
        "plt.xlabel('Normalized cum_kh')\n",
        "plt.ylabel('Normalized cum_h')\n",
        "plt.title('Lorenz Plot: Area b/w plots gives hetrogeneity!')"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Lorenz Plot: Area b/w plots gives hetrogeneity!')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bJgEBATz44IO0aNGC11577Yoej9mzZ/P4448D8N5779GiRQtatGjBBx/8W0Uyp6clLCyMrl27MmDAAJo1a1botgghbk52tiY5PYPTiWkcPpvMzhMJbN31N0nHdxZbDPY+a70WcNLidbQ57HTeCZVSYzD22vH19SUsLOyK8RUrVsz9orSnvDEMGzaMGTNm0KVLF15//XVefPFF3nrrLbKyskhJSWHNmjVkZGTQt29fFi5cSLVq1fjpp5+YNGkSn376KVlZWaSmprJ69WpWrlzJ1KlTWbx4cW6CPXHiBIMGDeLee+/Nd/uTk5PJzMxkyZIl3HbbbbnTJCcn89tvvxEeHs6GDRuIj48nNDSUtm3bMnXqVD788EN+/PHHq7Zp3rx51KhRI/cHxX333cd9993H0KFDmTNnDo8++igLFiwgIyODM2fOsHLlSlxdXa9YhoeHBz///DPu7u4cOXKE0aNHs3bt2iviTklJ4fDhw3z55Ze89957jBgxgrlz5zJkyBBGjx7N+++/T6NGjdi+fTtjx45l6dKl9O3blz59+nD33XcDcOedd+Y7Xd7YOnXqlO//qCCXLl1i7969rFy5Eg8PDx566CHGjh1Lp06dOHnyJAMHDmTHjh1Mnz6dGTNm0LFjR1JSUnBzc8vtNVi9ejVaa+6//35WrFhB7dq1OXz4MJ9++ikzZ84kLi6Onj17MnXq1Nx2nzhxIuvWreOrr77Knb9Hjx4EBwfTqlWr3P9VamoqO3fuZMuWLdSvX/+q90V6evpVnx9nlJKSUiK2w1GU9vbUWpOaCXFp2cSnaeLTNEmXNelZmrRMSMvUpGf++zwtE9IzNelZ/y7DjUxGuy7nyTK/UE95svavlmgX26dZeydyq2mtPwc+BwgODtZ572G7a9eu3Et7Xlmyj/2nkop0/c1qVuTlO5tfczrLy4sSExNJSkqib9++AIwZM4bBgwfj5eWFq6srw4cPx8vLi71793LgwAEGDhwIGHuiNWrUyJ1uyJAheHl50bVrVyZPnpy7jvT0dEaNGsXMmTNp3vzq2NLS0ujatStg7JE/9thjlC1bNjfO8PBwhg0bhre3N97e3oSGhnLgwAEqVqyIUqrQS6Vyxm3fvp3Fixfj5ubGI488wtSpU/Hy8sLNzY0HHngAb2/vq+bNzs7m8ccfZ/fu3bi6unLo0KGr1uXp6Ym/vz+33HILAB06dODs2bMopdi6dSujRo3KnfbSpUu56/Tw8MDLy4uUlJRCp8uJrbD/UUHKlSvH3XffjY+PD2DseR8+fDh3fEpKCkopunXrxksvvcTQoUMZNGgQnp6ebNiwgTVr1nDrrbfmThsTE0NgYCD16tWjZ8+eue3bqFEj9u3bR+PGjTly5Ai9evXio48+4p577sHPzw+Ae++9l507d9KlS5fc+cqXL09ISAhBQUH5xu/u7k6bNm0K3D5nIfcGL1olvT2zszWxyZeIuZBKdEIapy6kE3MhlZiENGIupBGTkM7Fy1lXzOPqoqhQ1hUvdzc8y5XB07MM1cqVwdO9DJ5lzb/lyuDlXoYGKbtov/8NvJKPklC3N1sq3cPdPW4rlm2zdyKPAepYvK5tDisVcirjaK1p3rw5mzdvzne6cuXKAcbJa5bHPMeNG8egQYO47bb83yw5x8jtpaDKP++//z6+vr78/fffZGdn4+7unu90OdsNxranpaWRnZ2Nt7f3NbfrWtPdbFUiy/mzs7PZsmXLVdsxefJk+vfvz/Lly7nlllv4+eef0Vrz/PPPM3bs2CumjYqKuiqmIUOGsGjRIgIDAxk4cOB1nXEuVaxEaZSdrTmVmMaR2JTcR1T8RU5dSOd0YhoZWfqK6b3Lu1HL24P6VStwS6Nq1PL2MB6Vjb9VKpS99ucu+Sz8MQX2fA/edeGB76kc0AfvYuzdsHciXww8rpRaCHQAErXWV3WrXy9r9pyLQ6VKlahcuTLr16+na9euzJkzh27dul01XUBAAOfOnWPz5s106tSJjIwMDh06lO9edo6ZM2eSnJzM5MmTbzi+rl278tlnnzFixAjOnz/PunXrmDFjBjExMaSkpFi1jM6dO7Nw4UKGDx/OvHnzcnsACpOYmEjt2rVxcXHh22+/JSsr65rz5KhYsSL+/v788MMPDB48GK01e/bsoVWrVnh5eeV2Ixc2nSVr/0eF6d27Nx9//HHuMe3du3fnHqcPCgoiKCiI7du3c+jQIW6//XamTJnC0KFD8fT0JCYmpsC7rA0cOJA33niDXbt25Xb1d+3alZEjRzJ58mS01vzyyy/MmTPnuuIVwtllZmVzMsE4Jn3kXApHzqYYf2NTSLXYq65aoSz1q1WgdR1v+reskZuka3t7UNPbgwrlbiIFZmfBjq9h9WuQmQa3ToQuE6Bs+SLYwutj00SulFoAhALVlFLRwMuAG4DWehawHOgHHAFSgVH5L8k5pKamUrt27dzXEyZM4Ntvv2XcuHGkpqbSoEEDvvnmm6vmK1u2LD/++CNPPvkkiYmJZGZm8tRTTxWayN955x3c3Nxo3bo1YOydjxs37rriHThwIJs3b6ZVq1YopXj77bfx8/OjatWquLq60qpVK0aOHMnTTz9d4DI+/vhjRo0axYwZM6hevXq+25fXo48+yj333MN3331Hnz59rnvvcd68eYwfP57XX3+djIwMhgwZQqtWrRgyZAiPPPIIH330ET/++GOB0+VV0P9o6tSpBAcHM2DAgELj+eijj3jsscdo2bIlmZmZ3HrrrcyaNYsPPviANWvW4OLiQvPmzenVqxfVqlXjwIEDdOrUCTAOIcydOxdXV9erllu5cmWaNm3K/v37CQkJAaBt27aMHDky9/XDDz9cIrrJhcjP5cxsIuMucjg2mSOxKRyOTeFobArHzl3kcta/V3bUqOROIx9P7m9fh0Y+njT28aKRjydVKpS1TWDR4bDsaTj9NzQIhX7vQrVGtlmXFZTW+tpTOZjg4GCdtx75rl275AutCNn7dqIlkaO06YEDB2jatKm9w7hpJf2YbnGzZ3tmZGVzPP4ih86mcOhsMofPpnDwbDJRcRfJzDZylFJQp3J5Gvt40sh8NPb1omH1Cni5F1PtgNTzsPpVCJ8Nnr7Q501oPtAILg8b1CMP11oH5zfO3l3rQgghSonMrGyOn0/l8NnkK5L2sbiU3OPXSkHdKuVp7ONF72a+NPH1yk3c7m5X91wVC61h93zjWHjaBej4KIROBveK9oknD0nkQgghipTWxhniB04nEXEmmYjTSRw8m8LRcylczvy3S7xOFQ+a+HjRPdCHJr6eNPH1omF1TzzK2ilh5+fsPlj2DJzYDHU6QP93wS//K0LsRRK5EEKIG5aekcXhsykcOJNExOlkIs4Yyfv8xcu509Ss5E4TPy+6Nq5GE18vmvgae9jlyzpwCrqUDGHTYcv/wL0SDPgEWg8FF8crUeLArSiEEMJRaK05lZhOhLmXfeB0EgdOJxEZdxHzMDbubi4E+FWkdzNfAv28CKxRkaZ+FalUvpiOYRcFrWH/r7DiBUg+BW1HwG3ToHwVe0dWIEnkQgghrpCVrTl2LoV9p5LYG5PIvlNJ7D+dRGJaRu40dap4EOhXkf5BNQisUZFAPy/qVa2Aq4sTV9iLPwrLn4Wjfxnd5/d9B3Xa2zuqa5JELoQQpVh6RhaHziYTdjKDP3/9h70xSUScSSI9wziWXbaMC4F+XvQLqkGzGl40rVGRJn5eVCyuM8WLQ0YabHjfeJRxh75vQ/BocHWOFOkcUToJT09Pq2+kYk+hoaG88847BAfneyUDYFynXlAhlh9++IGpU6fi5+fHmjVrrnv9UVFR3HHHHezdm291WyGEjaRcymS/xV72vlOJHIlNyb3Ey6vcKZrWrMgDIXVpUbMSzWtVpGF1T9xcHe+4cJE5/IexF54QBUGDoffr4OVn76iuiyRyB5CZmUmZMo71r3j33XcLTORfffUVX3zxRe79va/FEbfvejh7/KJ0upyZzcEzyeyOvsDfJ43HkXMp5Nw6pJpnWZrXrESPQB+a16zExegD3NunOy7O3DV+PRKjYcVkOLAEqjWBBxdDg+u7q6OjKME/sxyDvcqYRkVFERgYyNChQ2natCn33nsvqampV8W3YMECgoKCaNGiBc899xxg3CM8LS2N1q1bM3To0Cumf/XVV9mwYQOjR49m4sSJuYVbgoKCaNOmTe4e+uzZsxkwYAA9evTILQRiqaASpYW119NPP01wcDBNmzZl+/btDBo0iMaNG/PSSy/lLregEp+vvfYaAQEBdOnShQceeIB33nmn0P9b3vgvXrzIQw89REhICG3atOG3334DYN++fYSEhNC6dWtatmx5RfEUIYqL1prIuIv8uiuGaYv3MfDTjbSYtpI7P9nAlF/3siYiljpVyvNUzyZ8PTKYrS/0ZPuLt/HtQyFM6hNI/5Y18CnvUjqSeOZl2PABfNIeDv8JPV+GcRudNokDxhvA2R7t2rXTee3cufOqYcWtQoUKVw0LCgrSYWFhWmutp0yZov/73/9qrbXu1q2bHj9+vNZa68uXL+tOnTrp2NhYrbXWCxcu1KNGjcqdbsKECVprrZctW6Z79ux5xfKjoqJ0YGCgjoqKumJ4ZGSkBvSGDRu01lqPGjVKz5gxI3eZ27dv1zExMbpOnTo6NjZWZ2Rk6O7du+tffvmlwG3JkTO/1lq/8847ubEeOHBA16lTR6elpelvvvlG16pVS8fHx181f2RkpHZ1ddW7du3SWms9ePBgPWfOnGu216RJk7TWWn/wwQe6Ro0a+tSpUzo9PV3XqlVLx8XF6R07dugWLVrolJQUnZycrJs1a6Z37typt23bplu1aqXT0tJ0UlKSbtSoUW5bFCRv/M8//3xujAkJCbpx48Y6JSVFP/7443ru3Llaa60vXbqkU1NTC1xmUlJSoessLvv377d3CEVizZo19g7Bbs4mpelV+87oGSsi9LAvt+igl1foes8t1fWeW6oDX/pdD561Sb+xbL9e8neMPhF/UWdnZ19zmaWiPSPXa/1JiNYvV9R6/gNaJxy32aqKuj2BHbqAnFgy+wt/nwxn/inaZfoFQd/p1zVLYmIiFy5cyC3CMWLECAYPHpw7/v777wfg4MGD7N27l169egH/ljHNMWjQIADatWtHVFRU7vD09HQGDx7Mxx9/TL169a5af506dXLLgA4bNoyPPvqIZ599Nnf89u3bCQ0NpXr16gAMHTqUdevW5dbztsaGDRt44oknAHJLcR46dAiAXr16UaVK/pds+Pv7594nPme7rtVeOfc8DwoKonnz5rlt1KBBA06ePMmGDRsYOHBg7r3bBw0axPr168nOzuauu+7C3d0dd3d37rzzTqu2zTL+VatWsXjx4tw9+fT0dE6cOEGnTp144403iI6Ozu0hEKIoaa05eu4i2yLPsz3qPNsizxNzwejBcnVRBPp50b9lTVrXqUSrOt40qu5JmZJ8TPtGpMTCqimwZ2FuhTIC+tg7qiJTMhO5k7B1GdO85feupwxmUSisGEp+JUqvJWceFxeXK+Z3cXG5ol2KimX8Wmt++uknAgICrpimadOmdOjQgWXLltGvXz8+++wzevToUeSxiNIjK1tz4HQSWyPPs91M3vHmzVWqeZajg38VRt1SnzZ1vWlWo5Jj3QXN0ThQhTJbKpmJ/Dr3nG3F3mVMT5w4kbvM+fPnX3VyWkhICE8++SRxcXFUrlyZBQsW5O5du7m5kZGRUWCJzRxdu3Zl3rx59OjRg0OHDnHixAkCAgLYuXNnofPl52ZLihZU4jMzM5OxY8fy/PPPk5mZydKlSxkzZsx1xXb77bfz8ccf8/HHH6OUyi3Sc+zYMRo0aMCTTz7JiRMn2LNnjyRycV0uZWaxJzqRbZHG3vbO4wkkXzJ+mNap4kG3gOp08K9CiH9V6lctX+w/yJ1WdDgsmwCnd5sVyt6BaiWzx6xkJnI7cbQypgEBAcycOZOHHnqIZs2aMX78+CvG16hRg+nTp9O9e3e01vTv35+77roLgJEjR9KyZUvatm3LvHnzCozj0UcfZfz48QQFBVGmTBlmz559xd7y9bKmvQpSWInPAQMG0LJlS3x9fQkKCqJSpUoAzJo1C+CaJWCnTP8igjQAACAASURBVJnCU089RcuWLcnOzsbf35+lS5eyaNEi5syZg5ubG35+frzwwgsA9OvXjy+//JKaNWtedxuIki09I4sdUQlsjYxna+R5dp+8kHv/8cY+ngxoXZMQ/yqE+FehRiUPO0frhNISjAplO74xKpTd+zU0H5RvhbKSQsqYllA3e622o5TcLCopKSl4enqSmprKrbfeyueff07btm2LNQZHaVMpY1q8srI1/8QksvFIHBuPxLHjeAKXM7NxUdCiViVC6lehvX8V2tevYrv62VZwlvYsUG6FsqlGMu8wzq4VyqSMqRBFbMyYMezfv5/09HRGjBhR7ElclB5aa47FXcxN3JuPxpOUbnSVB/p58WDHetzSqBrt/avgWU6+gouEZYWy2iFwx3sOV6HMluRdVELVr19f7pxmYf78+fYOQZRgsUnpbDwax8Yj8Ww8EsfpxHQAanl70LdFDW5pXI3ODatSzfPGDzuJfDhRhTJbkkQuhBDXKe1yFpuPxbH+sLHXfeiscWtm7/JudG5YlccbVaNLo2rUrSInp9mE1rD/N1jxvNNUKLOlEpXItdbyoRGiEM54ToyjiE5IZU1ELH9FxLLpaDyXMrMpV8aFEP8qDGpbmy6NqtGsRsXScXc0e4o/CssnwtHVTlWhzJZKTCLPysoiPj6eqlWrSjIXIh9aa+Lj43F3d7d3KE4hMyubXScvsPpALGsiYjl4NhmAelXL858OdekR6EP7+lVwd5PruItF3gplfd6C9g87TYUyWyoxLXDx4kWSk5M5d+6cvUMpEdLT0+ULv4g5Qpu6u7tfcYmkuNKF1MusPXSOvyJiCTt4jsS0DMq4KNrXr8JL/ZvSPdCHBtUqyM5CcTv8h7EXnhDptBXKbKnEJHKtNf7+/vYOo8QICwuTy/mKmLSp49Fac+hsCqsjzrImIpbw4wlka6haoSy3NfWlR6APXZtUK1m1t52JZYWyqo2dukKZLZWYRC6EENZIz8hi89F4/jKPd+fct7x5zYo81r0RPQJ9aFXbW45121NWBmz5FMLeAp0NPaZA5yegjJz1nx9J5EKIEu/UhTT+ijCOdW88Gkd6RjYebq50aVyNx3s0onuAD36V5FCSQ4jaaFwTfu4ABPSDPtOh8tVFocS/JJELIUqcrGzN7pMJ/BURy+oDsUScMU5Uq1PFgyHt69I90IcO/nKimkNJiTXuyvb3AqhUF4YsgMB+9o7KKUgiF0KUCImpGaw9fI41EbGEHYwlITUDVxdFcL3KPN83kJ5NfWhY3VNOVHM0lhXKMlKh67PQ9ZkSV6HMliSRCyGcktaaw2eTjb1u80S1rGxN5fJuhAb40CPQh1sbV6dSeTlRzWHFhMNSs0KZ/63Q712o3sTeUTkdSeRCCKeRnpHFlmPxrImIZdnuNOJWrgOgaY2KjOvWgB6BPrSuUxlXOVHNsaUlGHvgO74GTx+45ytocU+JrlBmS5LIhRAO7Uxieu4Z5huPxJGWkYW7mwsB3i483acp3QN8qOkt5T6dgtbw90JY9RKknTcqlHV/3rhPurhhksiFEA4lK1vzd/QF1pgnqu0/nQQYBUjubVebHk196NSgKls2rie0g5zN7DTO7jcrlG2C2u2h/y9Qo6W9oyoRJJELIewuKT2DdeYd1dYePEf8xcu4KGhXrzLP9QmkR6APTXzlRDWndCkF1poVysp5wYCPofWwUlehzJYkkQship3WmqPnLhp73RFn2RGVQGa2ppKHG6EB1ekR6EO3JtXxLl/W3qGKG6U1HFhsVChLioE2w+G2V6BCVXtHVuJIIhdCFJvTiWn8siuGn8KjOXruIgABvl48cqtxolqbOt6UcZU9NacXfxR+nwRH/gTfIBg8G+qE2DuqEksSuRDCptIuZ7Fq/xl+DI9mw5E4tIb29SszonN9egT6ULuyXC9cYmSkw8YPYP174FrWuCtb+0ekQpmNSesKIYqc1podxxP4KTyaZXtOk3wpk1reHjzRvRGD2tamfrUK9g5RFLXDf8LyZ40KZS3ugd5vQMUa9o6qVJBELoQoMtEJqfy8M4afd0YTFZ9K+bKu9G1Rg3va1aKjf1UpRFISJUYbx8EPLIaqjWD4r9Cwu72jKlUkkQshbkrq5Ux+/8foOt98LB6ATg2q8niPxvRt4UeFcvI1UyJlZRhnoodNB50lFcrsSD5hQojrltN1/v32k/z+z2kuXs6iXtXyTOjVhIFtalGnihz3LtGObzJurXruADTpA33fgsr17R1VqSWJXAhhtfSMLJb8fYrZm6LYdyoJz3JluLNVTe5pV5vgepXlOu+SLuWcWaFsvlQocyCSyIUQ1xSblM7cLceZt/UE8Rcv08TXkzcHBXF361p4lJVSoCVedhaEfwOrX4XLqdBlAtz6LJSVkxYdgSRyIUSBdp+8wDcbI1m25zRZWtMz0IdRt/jTuWFV2fsuLWJ2wrIJcGoX1O8K/d+F6gH2jkpYkEQuhLhCRlY2y/85zexNUew6cQHPcmV4sFN9HuxUTy4bK03SEuCv12H7V1KhzMHZPJErpfoAHwKuwJda6+l5xtcFvgW8zWkma62X2zouIcSVktIzmLP5ON9tjuJs0iXqVy3PtDubcW9wHTzlzPPSQ2vY871RoSw1HjqMhe4vSIUyB2bTT6dSyhWYCfQCooHtSqnFWuv9FpO9BCzSWv9PKdUMWA7Ut2VcQoh/JaVn8M2GKL7acIyk9Ey6Nq7Gm4OCCG3iI9d9lzaxB4wKZcc3Qq1gGPYT1Ghl76jENdj6Z3YIcERrfQxAKbUQuAuwTOQaqGg+rwScsnFMQgggMS2D2Rv/TeC9mvny356NaVFL9rxKnUspNDg6G9YtMSqU3fkhtHlQKpQ5CVsn8lrASYvX0UCHPNNMA1YppZ4AKgC32TgmIUq1xLQMvtkYyVcbIkmWBF66aQ0HlsCKydSVCmVOS2mtbbdwpe4F+mitHzZfDwc6aK0ft5hmghnHu0qpTsBXQAutdXaeZY0BxgD4+vq2W7hw4RXrSklJwdPT02bbUtpIexY9e7fpxQzNH8czWBmVQVomtPVx5a5GbtSr6JyXj9m7PZ2de9ppGh/+gqrnw0mpUJ+/64wgw6+tvcMqMYr6/dm9e/dwrXVwfuNsvUceA9SxeF3bHGZpNNAHQGu9WSnlDlQDYi0n0lp/DnwOEBwcrENDQ69YSFhYGHmHiRsn7Vn07NWmiWkZfL0hkq83GnvgvZv58mQJ2AOX9+gNyqlQtsOsUHb7m3iGjCFj/QZpzyJUnO9PWyfy7UBjpZQ/RgIfAvwnzzQngJ7AbKVUU8AdOGfjuIQo8fIm8NubGwm8eU3nTuDiJhz5E5ZJhbKSxqaJXGudqZR6HFiJcWnZ11rrfUqpV4EdWuvFwDPAF0qppzFOfBupbdnfL0QJJwlcXCUxBlY+D/t/kwplJZDNLw41rwlfnmfYVIvn+4FbbB2HECVd2uUsvt4Yyay1R0lOz6RPcz+e7NmYZjUrXntmUTJlZcDWWbDmTbNC2UvQ+UmpUFbCyF0ehHBymVnZ/Bgezft/HuJs0iV6BvrwTO8ASeCl3fFNxjXhsfulQlkJJ4lcCCelteaP/Wd5e+VBjsSm0KauNx8/0JYQ/yr2Dk3Y0xUVyupIhbJSQBK5EE4o/Ph53lwewY7jCTSoVoFZw9pye3M/KWRSmmVnQfhsWP2KVCgrZSSRC+FEjsSm8PaKCFbtP0t1r3K8MbAF9wfXoYyr3IGrVDu1C5ZOgFM7pUJZKSSJXAgncDYpnQ/+PMT3209SvmwZnunVhNFd/SlfVj7CpVraBbNC2ZdQoToM+hKC7pUKZaWMfAsI4cCS0jP4bO1RvtoQSVa25sFO9XmiRyOqespZx6Wa1rBnEax60ahQFjIGerwoFcpKKUnkQjigS5lZzN1ygk/+OkxCagYDWtXk2d4B1K1a3t6hCXuLjTArlG2AWu1g6I9Qs7W9oxJ2JIlcCAeSna1Z/Pcp3ll1kOiENLo0qsbkvoFOfztVUQQupcC6t2HzTCjrKRXKRC5J5EI4iHWHzjH99wj2n06iWY2KfPdQELc2qW7vsIS9aQ0RS+H3yZAUDW2GmRXKqtk7MuEgJJELYWd7YxKZ/nsEG47EUbuyBx/c35oBrWri4iInLJV65yPh90lweBX4NId7v4K6He0dlXAwksiFsJPohFTeXXWIX3bF4F3ejSl3NGNYx7qUK+OcZUVFEcpIh00fwfp3waUM3P5/EDIWXOUrW1xN3hVCFLPE1Axmhh1h9sYolILxoQ0Z160hlTzc7B2acARHVsPyZ+H8MWg+0EjiFWvaOyrhwCSRC1FMLmdpvlh3jE/WHCEpPYN72tZmQq8m1PT2sHdowhEknYIVz8P+X6FKQxj+CzTsYe+ohBOQRC6EjeWcif7a+jTi0w/QrUl1JvcNpGkNKWoiMCuUfQZhb0J2JnR/CW6RCmXCepLIhbChTUfi+L/fD7A3Jol6FV34cGgIXRrL2cbCdHyzWaFsHzTuDX3fhir+9o5KOBlJ5ELYQMSZJKb/HkHYwXPU8jbORK944ZAkcWG4GAd/vAy750LF2nD/PAjsL7dWFTdEErkQReh0YhrvrTrEjzuj8SpXhhf7NWV4p3q4u7kSFnbY3uEJe8vOhp2z4c9X4HIKdHkabp0oFcrETZFELkQRSErPYFaYcU90reHhLv481r0R3uXL2js04ShO7YZlEyAmHOp1MSqU+QTaOypRAkgiF+ImXM7MZt7W43y02rgn+t2ta/JM7wDqVJF7ogtT2gVY84ZRoax8NRj0BQQNlm50UWQkkQtxA7TWrNx3hjd/j+B4fCqdG1blhX5N5Z7o4l9awz8/wMoXITUO2j8M3V8ED297RyZKGEnkQlynY+dSeHnxPtYfjiPA14vZo9rTrUl1lOxhiRznDhpno0etNyuULYKabewdlSihJJELYaXUy5nMXHOEz9cdw72MK9PubMawjvUo4yrVp4Tp8kVY+zZs/sSoUHbH+9B2pFQoEzZlVSJXSjUBJgL1LOfRWstth0SJZ3Sjn+W1pfuJuZDGoLa1eL5vU6p7yQ07hElriFgGKyZD4kloPQx6SYUyUTys3SP/AZgFfAFk2S4cIRxLZNxFpi3ex9pD5wj082LR2E6E+Fexd1jCkZyPhN+fg8MrwacZjFoB9TrZOypRilibyDO11v+zaSRCOJBLmVnMCjvGzDVHKFfGhal3NOPBTtKNLixkXoKNH8H6d4wKZb3fgA5jwVWK34jiVWgiV0rl7HosUUo9CvwCXMoZr7U+b8PYhLCLbZHnef7nPRw9d5E7W9VkSv+m+FR0t3dYwpEc/QuWPQvnj0Kzu40KZZVq2TsqUUpda488HNBAzum4Ey3GaaCBLYISwh4SUzOYvuIAC7adpHZlD2aPak9ogI+9wxKOJOkUrHwB9v0CVRrAsJ+g0W32jkqUcoUmcq21VXfvV0r10lr/UTQhCVG8tNYs3XOaV5bsJyH1MmNubcBTtzWmfFm5qEOYsjJh22ew5v+MamWhL8At/wU36akR9ldU31RvAZLIhdOJTkhlyq97WXPwHEG1KjF7VHu5qYu40oktxjXhZ/dCo17Q721jb1wIB1FUiVzuhCGcSmZWNrM3RfHuqkMoBVPuaMYIOZlNWLoYD39OhV05FcrmQuAdcmtV4XCKKpHrIlqOEDa3NyaRyT/vYW9MEj0CfXjt7hbU8vawd1jCUWRnw85vYfUrcCnZ6EK/dRKU87R3ZELkSw4CilLj4qVM3v/jEF9vjKSqZzlm/qct/YL85Naq4l+n/4alEyBmh1mh7B3waWrvqIQoVFEl8qgiWo4QNrEmIpaXft1LzIU0/tOhLs/1CaSSh1zvK0zpifDXG7D9CyhfFQZ+Di3vk2504RSsvUWrK9AfqM+Vt2h9z/w7yBbBCXGzLqRe5qVf97J0z2ka+Xjyw7hOtK8vd2YTJq3hnx+NS8ounjMqlPV4SSqUCadi7R75EiAd+AfItl04QhSdPdEXGD93J7HJ6Uzo1YSx3RpQroyrvcMSjsKyQlnNtlKhTDgtaxN5ba11S5tGIkQR0Vozb+sJXl2yn+pe5fhxXGda1ZE9LGG6fBHWzYBNn0DZ8tD/PWg3ElzkR55wTtYm8t+VUr211qtsGo0QNyn1ciYv/rKXX3bF0K1JdT64vzWVK5S1d1jCEWgNB5cbBU4ST0Kr/0CvV8Gzur0jE+KmWJvItwC/KKVcgAyM68a11rqizSIT4jodPZfC+LnhHI5NYUKvJjzevREuLnKykgASoowEfmiFWaHsd6jX2d5RCVEkrE3k7wGdgH+01nLNuHA4y/85zaQf91C2jAvfPRRC18aylyUwKpRt+gjWvQPKFXq/Dh3GSYUyUaJYm8hPAnsliQtHk52tmb4igs/XHaNNXW9m/qctNeXmLgLg6BpY/izEH4Fmd8Htb0qFMlEiWZvIjwFhSqnfubKM6Xs2iUoIK6RnZPHMor9Z9s9phnesx5Q7mlG2jNxitdRLOm1WKPtZKpSJUsHaRB5pPsqaDyHs6kLqZcZ8F862qPO81L8po7v4yx3aSrusTNj2uVmh7LJUKBOlhlWJXGv9yo2uQCnVB/gQcAW+1FpPz2ea+4BpGPds/1tr/Z8bXZ8o+aITUhn5zXZOxKfy8QNtuLNVTXuHJOztxFZYNsGsUHYb9JshFcpEqWHtnd3WkE9hFK11j2vM5wrMBHoB0cB2pdRirfV+i2kaA88Dt2itE5RSPtcRvyhl9p1KZOQ327mUkcV3o0Po2KCqvUMS9nQxHv58GXbNgYq14L450PROubWqKFWs7Vp/1uK5O3APkGnFfCHAEa31MQCl1ELgLmC/xTSPADO11gkAWutYK2MSpcy6Q+cYPzecSh5uzBvfmSa+XvYOSdhLdjY1Tq2CT0YaFco6PwndnpMKZaJUsrZrPTzPoI1KqW1WzFoL44z3HNFAhzzTNAFQSm3E6H6fprVeYU1covT4MTyayT/tobGvF7NHtce3ohz3LLVO/w3LniEgejvU7Qz93wXfZvaOSgi7sbZr3bLKhAvQDqhUhDE0BkKB2sA6pVSQ1vpCnhjGAGMAfH19CQsLu2IhKSkpVw0TN85R2lNrzZJjGfx8OIPmVV14vHkmB3Zu4YC9A7sBjtKmzso18yL+kfOpFbOcDDcv9vmPI7FuHzgQazzETZH3Z9Eqzva0tms9HOMYucLoUo8ERlsxXwxQx+J1bXOYpWhgq9Y6A4hUSh3CSOzbLSfSWn8OfA4QHBysQ0NDr1hIWFgYeYeJG+cI7ZmZlc2U3/bx8+ETDGpTi+n3tHTqy8scoU2dktaw9yfjkrKUWGg/mrI9XiJx69/SnkVI3p9Fqzjb09qudf8bXP52oLFSyh8jgQ8B8p6R/ivwAPCNUqoaRlf7sRtcnyghUi9n8vj8XfwVEctj3RvybO8AubysNDp3CJY/A5HrjMpkDyyEWm3tHZUQDsXarvXHgHk53d1KqcrAA1rrTwubT2udqZR6HFiJcfz7a631PqXUq8AOrfVic1xvpdR+IAuYqLWOv/FNEs4uLuUSD83ezt6YRF6/uwXDOtazd0iiuF1OhfXvwMaPzApl70K7UVKhTIh8WNu1/ojWembOC/MysUeAQhO5Oe1yYHmeYVMtnmtggvkQpVxk3EVGfL2N2OR0Ph8ezG3NfO0dkihuETkVyk5IhTIhrGBtIndVSqmce62b14fLHd5Ekdp5IoHRs7ejlGLhmE60lhripUvCcbNC2e9QvSmMXA71b7F3VEI4PGsT+Qrge6XUZ+brseYwIYrEqn1neGLBLvwqufPtqBDqV6tg75BEccm8BJs+NiuUuUCv16DjeKlQJoSVrE3kz2Fc+jXefP0H8KVNIhKlzqLtJ5n88x6Canvz9YhgqnqWs3dIorgcC4Nlz0L8YWg6APq8CZVq2zsqIZyKtWetZwOzzIcQRWbW2qNM/z2CW5tUZ9awtpQva+1vS+HUks/Ayhdh749Q2R+G/giNe9k7KiGcknxrCrvQWjP99wg+W3eMO1rW4L37Wjv1NeLCSlmZsP0L+OsNo0JZt8nQ5WmpUCbETZBELopdZlY2z//8Dz+ERzO8Yz2mDWiOq4tcI17indwGSyfA2X+gYU+jQlnVhvaOSginJ4lcFKv0jCyeWLCLP/af5b89G/PUbY3lRi8lXep5o0LZzu/Aqybc951xPFz+70IUiUITuVJqCfmUL82htR5Q5BGJEispPYNHvt3B1sjzvDKgOSM617d3SMKWsrNh91z442VIT4TOT5gVyqRqnRBF6Vp75O+YfwcBfsBc8/UDwFlbBSVKnnPJlxj5zTYOnknmwyGtuat1LXuHJGzp9B5Y9gxEb4O6naD/e1KhTAgbKTSRa63XAiil3tVaB1uMWqKU2mHTyESJcfJ8KsO/2sqZpHS+HBFMaICPvUMStpKeBGv+D7Z9Bh5V4O7/QasHpBtdCBuy9hh5BaVUA631MQCzCIrcsUNc08EzyTz49VbSM7KZ93BH2tWrbO+QhC3kVih7EVLOQvBD0HMKeMj/WwhbszaRPw2EKaWOYZQyrYdxdzchChR+/DwPzd5BuTIuLBrbiQA/OTZaIsUdNrrRI9dCjdbwwHyo1c7eUQlRalh7Q5gVSqnGQKA5KEJrfcl2YQlnt+ZgLOPnhuNX0Z05oztQp0p5e4ckipplhTK38tDvHWNPXCqUCVGsrC1jWh6jOlk9rfUjSqnGSqkArfVS24YnnNFvu2N4ZtHfBPh5MXtUCNW95JarJc7B3+H3SXDhBLQcAr1fA08590EIe7C2a/0bIBzoZL6OAX4AJJGLK3y7KYppS/YRUr8KX4wIpqK7FL4oURKOw4rJcHA5VA+Ekcugfhd7RyVEqWZtIm+otb5fKfUAgNY6VcldPIQFrTUf/HmYD1cfplczXz5+oA3ubtLFWmJkXobNH8PaGcYZ6L1ehY6PSoUyIRyAtYn8slLKA/PmMEqphoAcIxeAkcRfX3aArzZEcm+72kwfFEQZV7lveolxbC0sfxbiDkHTO+H2N8G7jr2jEkKYrE3k0zDqj9dRSs0DbgFG2igm4USysjUv/foPC7adZGTn+ky9oxkuct/0kiH5DKx6Cf75ASrXh//8AE162zsqIUQe1p61vkopFQ50xLj87L9a6zibRiYcXkZWNs/+8De/7T7FY90b8mzvALlvekmQlQnbv4Q1b0BmunFb1S5Pg5uHvSMTQuTD2rPWVwPvaq2XWQz7XGs9xmaRCYdmWfxkUp8AHg1tZO+QRFGI3gFLn4Yze6BhD+OSMqlQJoRDs7Zr3R94TinVXmv9ijksuLAZRMmVejmTsXPCWX84ToqflBSp5+HPabDzW6NC2eBvodldcmtVIZyAtYn8AtAT+MisiDbMdiEJR5aUnsHo2dsJP57AjHtbMjhYTnpyatnZsHse/DHVqFDW6XEInSwVyoRwItYmcqW1zgQeVUqNBDYAchPlUub8xcuM+HobB04n8fEDbenfsoa9QxI348xeWDYBTm6FOh3hjvfAt7m9oxJCXCdrE/msnCda69lKqX+Ax2wTknBEsUnpDPtqK1HxqXz+YDt6BPraOyRxo9KTIGw6bJ0FHt5w16dGhTIXuWRQCGdUaCJXSlXUWicBPyilqliMigSetWlkwmFEJ6Qy9MutnEu+xOxR7encsJq9QxI3QmvY9wusfMG4tCx4FPSYAuWrXHteIYTDutYe+XzgDozbs2qMS89yaKCBjeISDuLYuRSGfbmVlEuZzH24A23ryhEVpxR3BJY/A8fCoEYruH8e1JYKZUKUBIUmcq31HeZf/+IJRziSiDNJDPtyG1prFozpSPOalewdkrheGWmw/l3Y+CGU8ZAKZUKUQNfqWm9b2Hit9c6iDUc4imMXsvjvZ1vwcHNl7sMdaeTjae+QxPU6uMKsUHYcWt4PvV4DLzm3QYiS5lpd6+8WMk4DPYowFuEgth6L5+3t6VSv5MH8hztKLXFnc+EErHgeIpZCtQAYsRT8u9o7KiGEjVyra717cQUiHMPaQ+cYO2cHld0VP4ztjF8ld3uHJKyVeRk2fwLrZhivb5sGHR+DMmXtGZUQwsasvfwMpVQLoBmQ+82utf7OFkEJ+1gTEcvYOeE08vFkXNNMSeLOJHIdLHvGqFAWeAf0eRO869o7KiFEMbD2XusvA6EYiXw50BfjpjCSyEuIvyLOMm7OTpr4eTJvdEd2bdto75CENZLPmhXKFoF3PfjPImhyu72jEkIUI2v3yO8FWgG7tNajlFK+wFzbhSWKU94kXqm8m71DEteSnQXbv4K/XjMqlN06CbpOkAplQpRC1ibyNK11tlIqUylVEYgF5CbbJcDqA2cZP3cnAX5ezB3dQZK4M7CsUNagu3FJWTWpPidEaWVtIt+hlPIGvsC4OUwKsNlmUYliIUncyaSeh9WvQPi34OUHg2dDs7ulQpkQpZxViVxr/aj5dJZSagVQUWu9x3ZhCVtbfeAs4+aG07RGReY8JEncoWVnw9/zjQplaReg02NSoUwIket6zlpvCdTPmUcp1Uhr/bON4hI2JEnciZzZa5yNfnKLUaGs/7vg18LeUQkhHIi1Z61/DbQE9gHZ5mANSCJ3Mn/uP8v4eWYSH92BSh6SxB3SpWRY86ZUKBNCXJO1e+QdtdbNbBqJsDlJ4k4gb4WydiOh51SpUCaEKJC1iXyzUqqZ1nq/TaMRNvPH/rM8Oi+cZjUq8p0kcccUdwSWPwvH1oBfS7h/LtQOtndUQggHZ20i/w4jmZ8BLmGUM9Va65Y2i0wUGUniDu6KCmXu0HcGtB8tFcqEEFaxNpF/BQwH/uHfY+TCCazad4bH5u+kWc1KfPdQiCRxR3NoJSyfaFQoCxoMvd+QCmVCiOti7Zkz57TWi7XWkVrr4zkPa2ZUSvVRSh1USh1RSk0uZLp7lFJaKSV9iUVEkrgDu3ASFg6F+fcZH00nXQAAGoNJREFUe+EjlsA9X0oSF0JcN2v3yHcppeYDSzC61gG41uVnSilXYCbQC4gGtiulFuc91q6U8gL+C2y9jthFIVbtO8Oj83bSvFYl5owOoaK7JHGHkHkZtsyEtW8br2+bJhXKhBA3xdpE7oGRwHtbDLPm8rMQ4IjW+hiAUmohcBeQ96S514C3gIlWxiMKIUncMXkn/AOzJkHcQalQJoQoMtdM5OZedbzW+tkbWH4t4KTF62igQ57ltwXqaK2XKaUkkd+kvyLO8th8SeIOxaxQ1loqlAkhbOCaiVxrnaWUusUWK1dKuQDvASOtmHYMMAbA19eXsLCwK8anpKRcNay02ReXxfs706nl6cKYJpfZueXGS5FKexYBnUWtmBX4R87FJfsyR2rczZlG/yH7VDk4FWbv6JyevEeLlrRn0SrO9rS2a323Umox8ANwMWegFbdojeHKKmm1zWE5vIAWQJgyCj/4AYuVUgO01jssF6S1/hz4HCA4OFiHhoZesaKwsDDyDitN/r+9ew+PorD+P/4+RhEQFOWmIAIiYEEQMFxEq6BUEVv8idavCFpUwNofWkvV2mKtWGmVFq/1igW8AVbbKgp4Q1DKTUAE5CIXAeXyFQSMhksIyfn+MYOGNJAN2d3Z3Xxez5Pn2Z2ZTD45JDnM7OycOZ9t5dEpH3JKneqMG9CJY48q32uuFb2e5bZ+HkwcDJsWwsldoMcINn6yXjWNI/2MxpfqGV/JrGesjbwysBU4r8iyWF4jnws0NbPGBA38SuCq73bgngPU2vfczKYBtxZv4nJw89dt57oxc6lfowov9O9Y7iYu5bBzG0y5B+aPgWp14fLR0PLScELZ+qjTiUgGinX62bWHsnN332tmg4C3gCxglLsvMbN7gHnuPuFQ9ivfW7w+h36jP6RW9SMZO6ATtaodGXWkiqmwEBaOg3d+H0wo6/SLYEJZ5aOjTiYiGS7WoSknAo8C+14rnw780t1LPcRw90nApGLL7jrAtl1iySOBZZu+4epRczi68hGMHdCJukdXjjpSxfTlkmBC2eezoEHHcEJZq6hTiUgFEeup9dHAWOCn4fO+4bIfJSKUlG7V5m/p+8wcKh+exbgBnahfo0rUkSqevG9h2n0w+wmofAz0/Bu06aMJZSKSVLE28truPrrI8zFmdksiAknp1n61g6tGzsHMeHFAR06qWTXqSBWLOyx9Fd78LXy7KZxQ9gdNKBORSMTayLeaWV9gXPi8N8HFb5JkX2zbyVUjZ5NfUMj4gWfSpHa1qCNVLFtXBxPKVr8XTCi74nlo0D7qVCJSgcXayK8jeI38QYKr1WcCh3QBnBy6TTm76PPMHHLz9jJ2QCeaH1896kgVR/4u+M+DwcfhleGi4ZB9PWTF+iskIpIYsV61vg7omeAschCbv91Nn5Fz2LZjDy/078hp9Y+JOlLFseJtmHwbbF+rCWUiknIO2sjNrMSry0Pu7n+Mcx4pwbYde+j7zBw25ezmues70KZBjagjVQxffwFv3gHL34BazYIJZY3PiTqViMh+Sjsi31HCsqOA64GaBMNOJIFydubT95k5rNu6k9H92tO+kS6oSriCfJj1GLx/f3Bh2/l/gDMHaUKZiKSkgzZydx+x73GRUaPXAuOBEQf6PImPb3fnc83oD1m1OZenrzmDzqfUKv2TpHzWzghurbplOTS/GC66TxPKRCSlxTL97DhgMNAHeBZo5+7bEx2sotu5Zy/XjZnLkg05PN6nHV2a14k6UmbL3Qxv/x4WjQ8ad+/x0PyiqFOJiJSqtNfI/wL0IhhW0srdc5OSqoLbnV9A/2fnMX/ddh7p3ZYLWh4fdaTMVVgA80bBlD9C/k744a3ww19DJb03X0TSQ2lH5L8G8oA7gSHhhDIAI7jYTTeSjrO8vQXc8Px8Zn22lQeuOJ0ft64XdaTMtWE+vDEYNn0Mjc8Nbq1aq2nUqUREyqS018h1r8kkyi8oZNDYBby/Ygv39WrFpW1PjDpSZtq1PTgCnzcqnFA2Clr2CieUiYikF93NIkXsLSjklpc+5p2lXzK0Z0uu7KALrOLOPZhQ9vbvYdc26HQjdPmtJpSJSFpTI08BhYXO7a8sYuKiTfyux6n8rHOjqCNlni+XhhPKZsKJHeDif8MJraNOJSJSbmrkEXN3hry6mH8t2MCvf9SMgec0iTpSZsnLhffvg1mPB0fePR+FNn01oUxEMoYaeYTcnaGvL2Xch1/w/7s24abzdaFV3LjD0tfCCWUbod01cP7dcFTNqJOJiMSVGnlE3J37Ji9nzMy19D+7Mbde0DzqSJlj62qYdBusngJ1W8EVz0KDDlGnEhFJCDXyiDzx/mqe+uAzru7UkCEX/wDTFdPll78L/vNQMKEsqxJ0vx/a99eEMhHJaPoLF4FJizcx/M1P+cnp9Rjas6WaeDysfCc4Ct++Bk67HC4cBtV1Ix0RyXxq5Em28Iuv+dVLH9PupBr85fLWHHaYmni55KwPJpQtex1qNoVrXoOTu0SdSkQkadTIk2jD17vo/9w8alc/kqevyabyEVlRR0pfBfkw+3GYdj94IZz3e+h8Exx+ZNTJRESSSo08SXLz9nL9mLns3lPA2P4dqVVNDeeQrZ0RvCd8yzJo3gO63wfHNow6lYhIJNTIk6Cg0Ll53AJWbs5ldL/2NK1bPepI6Sl3M7xzV3B3tmNOgivHwak9ok4lIhIpNfIkuHfiUt5bvpk//r/TOKdZ7ajjpB9NKBMROSA18gR7ftZaRs9Yy3VnNebqTjr9W2b7TSg7B3qMgNrNok4lIpIy1MgT6P0VW7j79aWcf2odhlz8g6jjpJf9JpTVgcv+DqddpgllIiLFqJEnyIovv2XQix/RtE41Hu7dliy9zSw27rBwPLx9ZzChrOPPoetvofIxUScTEUlJauQJ8FVuHteNmUvlSlmM6teeakeqzDHZb0JZe00oExGJgTpMnO3OL2DAc/P4KjePf9xwJvVqVIk6UurbN6Fs9hNwZHVNKBMRKQM18jhyd257ZRELPv+aJ/u2o/WJNaKOlNrcYdmEYELZNxug7dXQbagmlImIlIEaeRw9+O5KXl+4kdu7N6f7aSdEHSe1bV0Nk2+HVe8GE8p+OkYTykREDoEaeZy8umADj0xZyU/POJEbz20SdZzUlb8bZjwE0x8IJ5TdB+0HaEKZiMgh0l/POJi3dhu3v7KITicfx7BLW2ma2YGsfBcm3RpOKLsMLhgGR+vMhYhIeaiRl9PnW3cy8Pn51D+2Ck/2PYNKh+sCrf+Ssz54HXzZBKh5Clz9KjTpGnUqEZGMoEZeDjm78rl2zIcUFDqj+rWnRtVKUUdKLQX5wZXo0+4DL9CEMhGRBFAjL4e7XvuEdVt38vz1HWlc66io46SWdTODW6tuWQbNusNF98OxjaJOJSKScdTID9G7S7/ktY83cku3ppzZRG+X+k7ulnBC2VhNKBMRSQI18kOQsyufIa8u5tTjq/OLLqdEHSc1FBbA/NEw5R7YsxPOHgzn3AqVdKZCRCSR1MgPwZ8mLmPLt3mMvCZbF7cBbPgIJg6GjQug0Q/h4hFQu3nUqUREKgQ18jKavnILL837gp+f20R3btu1Hd67F+b+XRPKREQiokZeBjvy9nLHPxdzcu2juKVb06jjRMcdFr0UTCjbuRU63gBdf6cJZSIiEVAjL4Phby5nY84uXr7hTCofkRV1nGhsXhZMKFs3A+pnQ99/wgmnR51KRKTCUiOP0YdrtvHsrHX069yI7EbHRR0n+fJy4f37YfbjwYSynzwMba/RhDIRkYgl/K+wmXU3s0/NbJWZ3VHC+sFmttTMFpnZFDNrmOhMZbU7v4Df/HMRDY6rwu3dK9hFXO6wdAI81gFmPgKn94ZB8+GMfmriIiIpIKFH5GaWBTwG/AhYD8w1swnuvrTIZguAbHffaWY3AsOB/0lkrrJ68J0VrPlqBy/270jVShXoJMa2z2DS7bDqHah7Glw+Gk7qGHUqEREpItFdqQOwyt0/AzCz8cAlwHeN3N2nFtl+NtA3wZnKZOEXXzNy+mf07tCAs06pFXWc5MjfTcO142H6v4IJZRf+GToM1IQyEZEUZO6euJ2bXQ50d/f+4fOrgY7uPugA2/8N+F93v7eEdQOBgQB169Y9Y/z48futz83NpVq1anHNv7fQuXvmLnbkw7Czq1D1iMx/W9Wx2z6i2YqnqLL7f/myzg9Z3eRa9hypO9fFQyJ+Risy1TO+VM/4inc9u3btOt/ds0talzKHWGbWF8gGzi1pvbs/DTwNkJ2d7V26dNlv/bRp0yi+rLwefGcF63NXMqpfNuedWjeu+045ORvgrd/C0teg5iksbDaU03vdQoZ/10mViJ/Rikz1jC/VM76SWc9EN/INQIMiz08Ml+3HzLoBQ4Bz3T0vwZlismzTNzw2dRWXtq2f2U28IB/mPAlT/xxOKLsTOt/M9v/MijqZiIjEINGNfC7Q1MwaEzTwK4Grim5gZm2BpwhOwW9OcJ6Y7C0o5PZXFlGj6hHc9eMWUcdJnHUzg/eEb16qCWUiImkqoY3c3fea2SDgLSALGOXuS8zsHmCeu08A/gJUA1624Naen7t7z0TmKs3I6WtYvCGHx/u049ijMnDG+H4TyhpoQpmISBpL+Gvk7j4JmFRs2V1FHndLdIayWLU5lwffXcFFpx1Pj1YnRB0nvgoLYP4YmDJUE8pERDJEylzslgoKCp3f/HMRVStlMfSSllHHia+NC+CNwbDxI00oExHJIGrkRTw3ay3z123ngStOp071ylHHiY9dX4cTyp6Bo2pDr2eg1eWaUCYikiHUyEOfb93J8Dc/pWvz2lzatn7UccrPHRb9A94eEkwo6zAQzhuiCWUiIhlGjRxwd+741yKyDjOGXdoKS/ej1c3Lwwll/4H6Z0CfV6Bem6hTiYhIAqiRA+PnfsHM1Vv506WtqFejStRxDl1eLnwwHGY9BpWqaUKZiEgFUOEb+aacXQybuIzOTWrSu0OD0j8hFbnD8jdg8h3wzXpo2xe6DYWjKsi94UVEKrAK38ifm7WOgkLnvl6t0/OU+rY1MPl2WPk21GkJl/8dTuoUdSoREUmSCt/Ib7ugOT9pXY+TalaNOkrZ5O8O5oNPHwGHHQ4X/gk63KAJZSIiFUyF/6t/2GFGi3pHRx2jbFZNgUm3BvPCW/aCC4fB0fWiTiUiIhGo8I08rXyzEd78LSx9FY5rAlf/G5qcF3UqERGJkBp5OijIhzlPwbQ/Q+Fe6HonnHUzHH5k1MlERCRiauSpbt2scELZEmh6YTCh7LjGUacSEZEUoUaeqnZ8FUwo+/jFcELZWGjeQ7dWFRGR/aiRp5rCQvhoDLw7FPbkwtm/gnNu04QyEREpkRp5Ktm4IDiNvmF+MKGsx1+hzqlRpxIRkRSmRp4Kdn0NU4cFE8qq1oJeI6HVT3UaXURESqVGHqXvJpTdCTu/gvYDoOvvoEqNqJOJiEiaUCOPyn9NKHtZE8pERKTM1MiTbc8OeH84zPpbMKHsxw9Bu59pQpmIiBwSNfJkcYflE2Hyb4IJZW36wo80oUxERMpHjTwZtq0JGvjKt4IJZZc9Aw3PjDqViIhkADXyRNqbBzMe/n5C2QXDoOMNkHVE1MlERCRDqJEnyur3YOKtsG01tLw0GDOqCWUiIhJnauTx9s1GeOt3sOTfcNzJ0PdfcMr5UacSEZEMpUYeLwV74cOnYOqfwgllQ6DzzXBE5aiTiYhIBlMjj4fPZwfvCf/yE2h6AVw0XBPKREQkKdTIy2PHVnj3LljwAhx9IvzPC3Dqj3VrVRERSRo18kNRWAgfPQtThkLet3DWLXDu7ZpQJiIiSadGXlabFsIbg2HDPGh4Nlw8QhPKREQkMmrksdqdA+8Ng7kjoWpNuPRpaH2FTqOLiEik1MhL4w6LX4G3h0DuZmjfH867UxPKREQkJaiRH8yWT4Or0ddOh3rt4KqXoF7bqFOJiIh8R428JHt2wAd/hZmPQqWqcPEDcEY/OCwr6mQiIiL7USMvbt+EspwvoE0f6DYUqtWOOpWIiEiJ1Mj32b42aOAr3oQ6LeDaydCwc9SpREREDkqNfG8ezHwkOJVuWXDBvdDx55pQJiIiaUGN/L17g0be4hK48M9wTP2oE4mIiMRMjbzzTdD4XGjaLeokIiIiZXZY1AEiV62OmriIiKQtNXIREZE0pkYuIiKSxtTIRURE0ljCG7mZdTezT81slZndUcL6I83spXD9HDNrlOhMIiIimSKhjdzMsoDHgIuAFkBvM2tRbLPrge3ufgrwIHB/IjOJiIhkkkQfkXcAVrn7Z+6+BxgPXFJsm0uAZ8PHrwDnm2k2qIiISCwS3cjrA18Ueb4+XFbiNu6+F8gBaiY4l4iISEZImxvCmNlAYCBA3bp1mTZt2n7rc3Nz/2uZHDrVM/5U0/hSPeNL9YyvZNYz0Y18A9CgyPMTw2UlbbPezA4HjgG2Ft+Ruz8NPA2QnZ3tXbp02W/9tGnTKL5MDp3qGX+qaXypnvGlesZXMuuZ6FPrc4GmZtbYzCoBVwITim0zAfhZ+Phy4D139wTnEhERyQiW6J5pZj2Ah4AsYJS7DzOze4B57j7BzCoDzwNtgW3Ale7+WSn73AKsK7a4FvBV3L+Bikv1jD/VNL5Uz/hSPeMr3vVs6O61S1qR8EaeLGY2z92zo86RKVTP+FNN40v1jC/VM76SWU/d2U1ERCSNqZGLiIiksUxq5E9HHSDDqJ7xp5rGl+oZX6pnfCWtnhnzGrmIiEhFlElH5CIiIhVO2jVyTVOLrxjqOdjMlprZIjObYmYNo8iZLkqrZ5HtLjMzNzNdJXwQsdTTzK4If0aXmNnYZGdMNzH8zp9kZlPNbEH4e98jipzpwMxGmdlmM/vkAOvNzB4Ja73IzNolJIi7p80HwXvRVwMnA5WAhUCLYtv8AngyfHwl8FLUuVP1I8Z6dgWqho9vVD3LV89wu+rAB8BsIDvq3Kn6EePPZ1NgAXBs+LxO1LlT+SPGmj4N3Bg+bgGsjTp3qn4A5wDtgE8OsL4HMBkwoBMwJxE50u2IXNPU4qvUerr7VHffGT6dTXCbXSlZLD+fAH8kGNe7O5nh0lAs9RwAPObu2wHcfXOSM6abWGrqwNHh42OAjUnMl1bc/QOCG5kdyCXAcx6YDdQwsxPinSPdGrmmqcVXLPUs6nqC/11KyUqtZ3hqrYG7T0xmsDQVy89nM6CZmc0ws9lm1j1p6dJTLDW9G+hrZuuBScBNyYmWkcr6N/aQpM30M4mWmfUFsoFzo86SrszsMOABoF/EUTLJ4QSn17sQnC36wMxaufvXkaZKb72BMe4+wszOBJ43s9PcvTDqYFKydDsiL8s0NQ42TU2A2OqJmXUDhgA93T0vSdnSUWn1rA6cBkwzs7UEr5lN0AVvBxTLz+d6YIK757v7GmAFQWOXksVS0+uBfwC4+yygMsF9w6XsYvobW17p1sg1TS2+Sq2nmbUFniJo4nr98eAOWk93z3H3Wu7eyN0bEVxz0NPd50UTN+XF8vv+KsHROGZWi+BU+0GHLlVwsdT0c+B8ADP7AUEj35LUlJljAnBNePV6JyDH3TfF+4uk1al1d99rZoOAt/h+mtqSotPUgL8TnApaRThNLbrEqS3Gev4FqAa8HF4z+Lm794wsdAqLsZ4Soxjr+RZwgZktBQqA29xdZ+AOIMaa/hoYaWa/IrjwrZ8OhkpmZuMI/iNZK7ym4A/AEQDu/iTBNQY9gFXATuDahOTQv4+IiEj6SrdT6yIiIlKEGrmIiEgaUyMXERFJY2rkIiIiaUyNXEREJI2pkYuIiKQxNXKRJApHl44o8vxWM7s7yRmm7bubnJlNMrMa5dxfFzN7Iz7pDjnD2vCGMEWXNTrQeEmRTKJGLpJceUCv4k0nVuFth+PG3XvovuQi6U2NXCS59hLMe/5V8RXhEeR7ZrbIzKaY2Unh8jFm9qSZzQGGh8+fCKd9fRYeEY8ys2VmNqbI/p4ws3lmtsTMhpYUZt+RrJn93Mw+Dj/WmNnUcP0FZjbLzD4ys5fNrFq4vLuZLTezj4BeB9h3lpn91cw+Cb+nm4p+zfBxtplNCx/fbWbPmtl0M1tnZr3MbLiZLTazN83siNKKa2ZVzGyymQ0IF2WZ2ciwBm+bWZXS9iGSbtTIRZLvMaCPmR1TbPmjwLPu3hp4EXikyLoTgc7uPjh8fixwJsF/CCYADwItgVZm1ibcZoi7ZwOtgXPNrPWBArn7k+7eBmhPMIjkgbDZ3gl0c/d2wDxgsJlVBkYCPwHOAI4/wG4HAo2ANkW+p9I0Ac4DegIvAFPdvRWwC7i4lM+tBrwOjHP3keGypgTzylsCXwOXxZBBJK2okYskmbt/AzwH3Fxs1ZnA2PDx88DZRda97O4FRZ6/Ht7/ejHwpbsvDsdMLiFongBXhEfMCwiafIsY4j1MMGjodYLpbC2AGWb2McEwoobAqcAad18ZZnjhAPvqBjzl7nvD73tbDF9/srvnh99XFvBmuHxxke/rQF4DRrv7c0WWrXH3j8PH82PYh0jaSauhKSIZ5CHgI2B0jNvvKPZ83zjZwiKP9z0/3MwaA7cC7d19e3jKvfLBvoCZ9SNo1IP2LQLecffexbZrQ/ns5fuDiOKZ8gDcvdDM8osM6yik9L9XM4DuZja2yOcVrU0BoFPrknF0RC4SgfDo9B8Es5/3mcn30/r6ANPL8SWOJmj+OWZWF7joYBub2RkEjb9veGQPwZjVs8zslHCbo8ysGbAcaGRmTcLtev/XDgPvADfsu0DPzI4Ll68lOCUP8T3VfRewneClC5EKQ41cJDojgKJXr98EXGtmi4CrgV8e6o7dfSHBKfXlBKfrZ5TyKYOA44Cp4QVvz7j7FqAfMC7MNAs41d13E7z+PTE8dX+gOfXPEMy2XmRmC4GrwuVDgYfNbB7BUXI8/RKoYmbD47xfkZSlMaYiIiJpTEfkIiIiaUwXu4lI2jCzfwONiy3+jbu/FUUekVSgU+siIiJpTKfWRURE0pgauYiISBpTIxcREUljauQiIiJpTI1cREQkjf0fLvRKeoo8R4wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEr6476PrV-9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Conclusion- Greater the area between the two plots- Greater would be the Reservoir Heterogeneity."
      ],
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8YOLkfaunFv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}